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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Building and operationalizing data processing systems	24%	- Data ingestion and integration 1. Streaming ingestion (Pub/Sub, Dataflow) 2. Batch ingestion pipelines (BigQuery, Cloud Storage) - Data processing and transformation 1. Using Dataproc, Dataflow, and BigQuery SQL 2. ETL/ELT pipeline design
Operationalizing machine learning models	26%	- ML pipeline integration 1. Feature engineering and feature stores 2. Vertex AI pipeline deployment - Model deployment and monitoring 1. Online vs batch prediction 2. Model monitoring and drift detection
Designing data processing systems	22%	- Data architecture and storage design 1. Designing scalable and cost-effective data models 2. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse) - Batch and streaming data processing design 1. Latency, throughput, and consistency trade-offs 2. Event-driven vs batch architectures
Ensuring solution quality	28%	- Security and governance 1. IAM and access control in GCP 2. Data governance and compliance - Reliability and performance 1. Fault tolerance and recovery strategies 2. Monitoring pipelines and workloads

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Google Certified Professional Data Engineer Exam Sample Questions (Q334-Q339):

NEW QUESTION # 334
Your team is building a data lake platform on Google Cloud. As a part of the data foundation design, you are planning to store all the raw data in Cloud Storage You are expecting to ingest approximately 25 GB of data a day and your billing department is worried about the increasing cost of storing old data. The current business requirements are:
* The old data can be deleted anytime
* You plan to use the visualization layer for current and historical reporting
* The old data should be available instantly when accessed
* There should not be any charges for data retrieval.
What should you do to optimize for cost?

A. Create an Object Lifecycle Management policy to modify the storage class for data older than 30 days to nearlme. 45 days to coldline. and 60 days to archive storage class Delete old data as needed.
B. Create the bucket with the Autoclass storage class feature.
C. Create an Object Lifecycle Management policy to modify the storage class for data older than 30 days to coldline, 90 days to nearline. and 365 days to archive storage class Delete old data as needed.
D. Create an Object Lifecycle Management policy to modify the storage class for data older than 30 days to nearline, 90 days to coldline. and 365 days to archive storage class. Delete old data as needed.

Answer: B

Explanation:
- Autoclass automatically moves objects between storage classes without impacting performance or availability, nor incurring retrieval costs. - It continuously optimizes storage costs based on access patterns without the need to set specific lifecycle management policies.

NEW QUESTION # 335
You want to encrypt the customer data stored in BigQuery. You need to implement for-user crypto-deletion on data stored in your tables. You want to adopt native features in Google Cloud to avoid custom solutions. What should you do?

A. Create a customer-managed encryption key (CMEK) in Cloud KMS. Use the key to encrypt data before storing in BigQuery.
B. Create a customer-managed encryption key (CMEK) in Cloud KMS. Associate the key to the table while creating the table.
C. Implement Authenticated Encryption with Associated Data (AEAD) BigQuery functions while storing your data in BigQuery.
D. Encrypt your data during ingestion by using a cryptographic library supported by your ETL pipeline.

Answer: B

Explanation:
To implement for-user crypto-deletion and ensure that customer data stored in BigQuery is encrypted, using native Google Cloud features, the best approach is to use Customer-Managed Encryption Keys (CMEK) with Cloud Key Management Service (KMS). Here's why:
Customer-Managed Encryption Keys (CMEK):
CMEK allows you to manage your own encryption keys using Cloud KMS. These keys provide additional control over data access and encryption management.
Associating a CMEK with a BigQuery table ensures that data is encrypted with a key you manage.
For-User Crypto-Deletion:
For-user crypto-deletion can be achieved by disabling or destroying the CMEK. Once the key is disabled or destroyed, the data encrypted with that key cannot be decrypted, effectively rendering it unreadable.
Native Integration:
Using CMEK with BigQuery is a native feature, avoiding the need for custom encryption solutions. This simplifies the management and implementation of encryption and decryption processes.
Steps to Implement:
Create a CMEK in Cloud KMS:
Set up a new customer-managed encryption key in Cloud KMS.
Associate the CMEK with BigQuery Tables:
When creating a new table in BigQuery, specify the CMEK to be used for encryption.
This can be done through the BigQuery console, CLI, or API.
Reference:
BigQuery and CMEK
Cloud KMS Documentation
Encrypting Data in BigQuery

NEW QUESTION # 336
Your company is in a highly regulated industry. One of your requirements is to ensure individual users have access only to the minimum amount of information required to do their jobs. You want to enforce this requirement with Google BigQuery. Which three approaches can you take? (Choose three.)

A. Restrict access to tables by role.
B. Segregate data across multiple tables or databases.
C. Ensure that the data is encrypted at all times.
D. Disable writes to certain tables.
E. Use Google Stackdriver Audit Logging to determine policy violations.
F. Restrict BigQuery API access to approved users.

Answer: A,E,F

Explanation:
Topic 1, Main Questions Set C

NEW QUESTION # 337
You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?

A. Instead of feeding in each feature individually, average their values in batches of 3.
B. Eliminate features that are highly correlated to the output labels.
C. Combine highly co-dependent features into one representative feature.
D. Remove the features that have null values for more than 50% of the training records.

Answer: C

NEW QUESTION # 338
You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the data. Which two actions should you take? (Choose two.)

A. Increase the number of nodes in the Cloud Bigtable cluster
B. Modify your Cloud Dataflow pipeline to use the Flatten transform before writing to Cloud Bigtable
C. Modify your Cloud Dataflow pipeline to use the CoGroupByKey transform before writing to Cloud Bigtable
D. Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkers in PipelineOptions
E. Configure your Cloud Dataflow pipeline to use local execution

Answer: B,C

NEW QUESTION # 339
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The Google sector is an ever-evolving and rapidly growing industry that is crucial in shaping our lives today. With the growing demand for skilled Google professionals, obtaining Google Certified Professional Data Engineer Exam (Professional-Data-Engineer) certification exam has become increasingly important for those who are looking to advance their careers and stay competitive in the job market.

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